Project Summary/Abstract Patterns of neural activity underlie information processing in the brain. Most work to date has focused on separate stages of computation by looking at separate regions in the brain - one at a time. We propose that techniques from graph theory can help us better understand how information is processed by entire populations of neurons. To this end, we use the nematode Caenorhabditis elegans to study the processing of an ecologically-relevant signal in most of the nervous system at once. Specifically, we will record activity from all of the neurons in the head of the worm, where most olfactory processing occurs, while we expose the animal to an innately attractive odor, diacetyl. We will then test how this representation changes in two behavioral states ? after adaptation, at which point the worm no longer finds diacetyl attractive, and when C. elegans recovers from adaptation, when it again finds diacetyl attractive. We will do all of this in a transgenic worm which will allow us to identify all neurons by name, and thus to analyze our results based on the known anatomical connections between neurons. Work we are preparing to submit for publication has established that one graph-theoretic feature can identify a stimulus? valence, i.e. whether or not it is attractive or repellent, and we will determine which neurons are driving changes in this feature. Finally, we will optogenetically test the predictions from our analyses to ensure that they are biologically significant. For instance, some non-overlapping subsets of neurons may represent positive and negative valence, and their activation may induce either forward, or backward, movements, respectively. If, for example, a neuron provides an important link between neurons that represent any valence (i.e. it is on the shortest path between these neurons) and the motor command interneurons, then we might reason that it facilitates the transfer of information, and that inhibiting it would delay the animal?s odor-seeking behavior. A future extension of this work would combine graph theory and information theory to understand how efficiently neurons process and transfer information. Importantly, this field, called network coding, proposes that an efficient way to transmit information is to allow downstream nodes to decode information that is processed along the path. During my postdoctoral work, as I gain experience with new theories and a new neural system that uses spiking neurons, I seek to develop the field of network coding for neuroscience. I am interested in spiking neurons to ensure my work is applicable to the larger field of neural systems which employ spiking, not graded, potentials.